There are a few notable counter-examples (e.g. HAL) but this is basically right for a lot of pre-2000 sci-fi. I think getting AI right from e.g. somewhere in the ~1970s would require a fairly complicated set of inferences, probably the biggest things would be: Image the world in 50 years. 1. "Digital knowledge work / computing". The vast majority of knowledge work (which makes up a very large fraction of the economy) goes digital. As computers get cheaper, much of society’s work happens on screens, over billions of machines in homes and offices. 2. "Internet". Similar to ARPANET (and see Licklider’s “Intergalactic Computer Network” (1963)), this creates demand for a global network of computers where people work, argue, teach, joke, and socialize. 3. "Big data". This creates a very large, compounding repository of digital information, with traces of how people reason, how they create, what they know etc. 4. "Statistical learning". Trying to program intelligence by hand is extremely difficult. But Shannon's n-gram approximations of English (1948) show examples of how language has statistical structure that can be "learned". You could imagine doing so over extensions of cybernetic computing (e.g. in the style of Rosenblatt's Perceptron 1958). So instead of programming thought, we could fit a statistical model to the record. 5. "LLMs". This might lead to something along the lines of ELIZA (1966), but with enough data (per 1-3), and enough compute (see Moore's law 1965), the imitation might become of extreme hight fidelity to the point that it's economically extremely useful. 6. "Scaling laws". These networks may demand a huge amount of data / computing resources and expertise to train. It's plausible that the more of each you have, the better your ELIZA gets. The economics concentrate, where a few organizations build a few minds and then deploy copies of them digitally. Something like that. Basically, it's a lot of leaps. You have to start with computers, to internet, to big data, to big compute (Moore's law), which then allow large-scale statistical learning approach based on neural networks, and then posit scaling laws, and then imagine the form factor of language models as a lot more sophisticated ELIZA. And you'd have to short everything else, most predominantly I think the idea that we'd figure out how to program robots by hand.

GPT-6 Astraの方がFable 5.1より少し賢いのかな？言わんとすることを理解してくれる度合いが違う。このプロンプトでどちらが賢いかお互いに評価させてみた：『建築と音楽は同じものとみなせる。フーリエ変換で連続と離散を切り替えるようなもの。』3者一致で僅差でAstraの勝利。

コーディングはAIで自動化できるようになったけど、マーケティングはまだまだ人間が自分でやる必要がある。Fable 5.5やGPT-6.1 Astraに期待しているのはマーケティングの自動化。その時のために今からAIに思う存分活躍してもらえる環境を構築しておいた方が良い。

2026年最大のイノベーション筆頭候補は当然Fable 5だけど、Omarchy（Linuxデスクトップ）もトップ3に入るかもしれない。 AstraはFableの後だし位置づけが難しいな。 Jev？まさか

AI分野での2025年最大のイノベーションは何か？ 確かにGPT-5.2 proやNanoBanana Proもすごいのだけれど、個人的にはCodexとClaude Codeではないかと思っている。 未来が見える人には共感してもらえるかな？

I'm officially done reading AI-generated code. It's been two weeks since I looked at any of it. I think the IDE is officially on its way to the graveyard. The job is no longer about "writing code," so we need new tools that better reflect this new reality. While reviewing the code, I realized my only complaints were stylistic, and I wasn't finding any obvious bugs anymore. The more code I generated, the harder it became to keep track of every line. I found that my time is better spent designing ways to verify that the overall system works than looking at the code. State-of-the-art coding agents are better at writing code than I'd ever be, and I'm going to stop pretending otherwise. I still think these coding agents can't go too far without an experienced human guiding them, but we're past the point where we need to check every line of code.

Starting August 14, auto mode will be the default permission mode in Claude Code for Pro, Max, and Team users. Auto mode reviews shell commands and actions with a separate classifier. In testing, it caught 89% of dangerous commands. Manual approval caught 14%.